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第一需要你更新 pip 版本需要使用'pip install --upgrade pip' command.
第二是 setuptools 版本太旧，所以出现以下问题Command "python setup.py egg_info" failed with error code 1 in /tmp/pip-build-G9yO9Z/tldr/，也是需要你更新
File "/tmp/pip-build-G9yO9Z/tldr/setuptools_scm-3.3.3-py2.7.egg/setuptools_scm/integration.py", line 9, in version_keyword File "/tmp/pip-build-G9yO9Z/tldr/setuptools_scm-3.3.3-py2.7.egg/setuptools_scm/version.py", line 66, in _warn_if_setuptools_outdated setuptools_scm.version.SetuptoolsOutdatedWarning: your setuptools is too old (<12)
----------------------------------------Command "python setup.py egg_info" failed with error code 1 in /tmp/pip-build-G9yO9Z/tldr/
You are using pip version 8.1.2, however version 19.2.1 is available.
You should consider upgrading via the 'pip install --upgrade pip' command.
##### 解决方法：
> （一）使用“pip install—upgrade pip”命令进行pip版本升级。
> [xiaokang@localhost ~]$ sudo pip install --upgrade pip
> （二）使用“ pip install --upgrade setuptools”命令进行setuptools 版本升级。
> [xiaokang@localhost ~]$ sudo pip install --upgrade setuptools
> 解决完以上问题你就可以成功安装上之前要安装的软件了
#### 2、ERROR: Cannot uninstall 'enum34' 。
##### 问题：
```python
在安装Airflow的时候，出现如下错误：
ERROR: Cannot uninstall 'enum34'. It is a distutils installed project and thus we cannot accurately determine which files belong to it which would lead to only a partial uninstall.sudo pip install --ignore-installed enum34
当出现其他无法升级的错误时，可以采用以下命令格式进行强制升级：
sudo pip install --ignore-installed +模块名
ERROR: Command errored out with exit status 1:
command: /usr/bin/python -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'/tmp/pip-install-oZ2zgF/flask-appbuilder/setup.py'"'"'; __file__='"'"'/tmp/pip-install-oZ2zgF/flask-appbuilder/setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' egg_info --egg-base /tmp/pip-install-oZ2zgF/flask-appbuilder/pip-egg-info
cwd: /tmp/pip-install-oZ2zgF/flask-appbuilder/
Complete output (3 lines):
/usr/lib64/python2.7/distutils/dist.py:267: UserWarning: Unknown distribution option: 'long_description_content_type'
warnings.warn(msg)
error in Flask-AppBuilder setup command: 'install_requires' must be a string or list of strings containing valid project/version requirement specifiers
----------------------------------------
ERROR: Command errored out with exit status 1: python setup.py egg_info Check the logs for full command output.
检查安装命令，一般此类问题是因为安装包找不到才会出现的错误。
ERROR: Command errored out with exit status 1:
command: /usr/bin/python -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'/tmp/pip-install-YmiKzY/setproctitle/setup.py'"'"'; __file__='"'"'/tmp/pip-install-YmiKzY/setproctitle/setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' install --record /tmp/pip-record-XTav9_/install-record.txt --single-version-externally-managed --compile
cwd: /tmp/pip-install-YmiKzY/setproctitle/
Complete output (15 lines):
running install
running build
running build_ext
building 'setproctitle' extension
creating build
creating build/temp.linux-x86_64-2.7
creating build/temp.linux-x86_64-2.7/src
gcc -pthread -fno-strict-aliasing -O2 -g -pipe -Wall -Wp,-D_FORTIFY_SOURCE=2 -fexceptions -fstack-protector-strong --param=ssp-buffer-size=4 -grecord-gcc-switches -m64 -mtune=generic -D_GNU_SOURCE -fPIC -fwrapv -DNDEBUG -O2 -g -pipe -Wall -Wp,-D_FORTIFY_SOURCE=2 -fexceptions -fstack-protector-strong --param=ssp-buffer-size=4 -grecord-gcc-switches -m64 -mtune=generic -D_GNU_SOURCE -fPIC -fwrapv -fPIC -DHAVE_SYS_PRCTL_H=1 -DSPT_VERSION=1.1.10 -I/usr/include/python2.7 -c src/setproctitle.c -o build/temp.linux-x86_64-2.7/src/setproctitle.o
In file included from src/spt.h:15:0,
from src/setproctitle.c:14:
src/spt_python.h:14:20: fatal error: Python.h: No such file or directory
#include
^
compilation terminated.
error: command 'gcc' failed with exit status 1
----------------------------------------
ERROR: Command errored out with exit status 1: /usr/bin/python -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'/tmp/pip-install-YmiKzY/setproctitle/setup.py'"'"'; __file__='"'"'/tmp/pip-install-YmiKzY/setproctitle/setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' install --record /tmp/pip-record-XTav9_/install-record.txt --single-version-externally-managed --compile Check the logs for full command output. 因为缺少python的开发包，yum install python-devel 安装即可解决
[2019-12-19 15:15:15,523] {taskinstance.py:1058} ERROR - bash /root/touch.sh
Traceback (most recent call last):
File "/usr/lib/python2.7/site-packages/airflow/models/taskinstance.py", line 915, in _run_raw_task
self.render_templates(context=context)
File "/usr/lib/python2.7/site-packages/airflow/models/taskinstance.py", line 1267, in render_templates
self.task.render_template_fields(context)
File "/usr/lib/python2.7/site-packages/airflow/models/baseoperator.py", line 689, in render_template_fields
self._do_render_template_fields(self, self.template_fields, context, jinja_env, set())
File "/usr/lib/python2.7/site-packages/airflow/models/baseoperator.py", line 696, in _do_render_template_fields
rendered_content = self.render_template(content, context, jinja_env, seen_oids)
File "/usr/lib/python2.7/site-packages/airflow/models/baseoperator.py", line 723, in render_template
return jinja_env.get_template(content).render(**context)
File "/usr/lib64/python2.7/site-packages/jinja2/environment.py", line 830, in get_template
return self._load_template(name, self.make_globals(globals))
File "/usr/lib64/python2.7/site-packages/jinja2/environment.py", line 804, in _load_template
template = self.loader.load(self, name, globals)
File "/usr/lib64/python2.7/site-packages/jinja2/loaders.py", line 113, in load
source, filename, uptodate = self.get_source(environment, name)
File "/usr/lib64/python2.7/site-packages/jinja2/loaders.py", line 187, in get_source
raise TemplateNotFound(template)
TemplateNotFound: bash /root/touch.sh由于airflow使用了jinja2作为模板引擎导致的一个陷阱，当使用bash命令的时候，尾部必须加一个空格
Running a worker with superuser privileges when the
worker accepts messages serialized with pickle is a very bad idea!
If you really want to continue then you have to set the C_FORCE_ROOT
environment variable (but please think about this before you do).
在/etc/profile 内添加 export C_FORCE_ROOT="True"
普通少量任务可以通过命令airflow unpause dag_id命令来启动，或者在web界面点击启动按钮实现，但是当任务过多的时候，一个个任务去启动就比较麻烦。其实dag信息是存储在数据库中的，可以通过批量修改数据库信息来达到批量启动dag任务的效果。假如是用MySQL作为sql_alchemy_conn，那么只需要登录airflow数据库，然后更新表dag的is_paused字段为0即可启动dag任务。
示例: update dag set is_paused = 0 where dag_id like "benchmark%";
出现这个情况的一般原因是scheduler调度器生成了任务，但是无法发布出去。而日志中又没有什么错误信息。
可能原因是Borker连接依赖库没安装：
如果是redis作为broker则执行pip install apache‐airflow[redis]
如果是rabbitmq作为broker则执行pip install apache-airflow[rabbitmq]
还有要排查scheduler节点是否能正常访问rabbitmq。
airflow的scheduler默认是起两个线程，可以通过修改配置文件airflow.cfg改进:
[scheduler] # The scheduler can run multiple threads in parallel to schedule dags. # This defines how many threads will run. #默认是2这里改为100 max_threads = 100
vi airflow.cfg
[core]
#logging_level = INFO
logging_level = WARNINGNOTSET < DEBUG < INFO < WARNING < ERROR < CRITICAL
如果把log的级别设置为INFO， 那么小于INFO级别的日志都不输出， 大于等于INFO级别的日志都输出。也就是说，日志级别越高，打印的日志越不详细。默认日志级别为WARNING。
注意: 如果将logging_level改为WARNING或以上级别，则不仅仅是日志，命令行输出明细也会同样受到影响，也只会输出大于等于指定级别的信息，所以如果命令行输出信息不全且系统无错误日志输出，那么说明是日志级别过高导致的。
这是由于airflow使用了jinja2作为模板引擎导致的一个陷阱，当使用bash命令的时候，尾部必须加一个空格:
t2 = BashOperator(
task_id='sleep',
bash_command="/home/batcher/test.sh", // This fails with `Jinja template not found` error
#bash_command="/home/batcher/test.sh ", // This works (has a space after)
dag=dag)任务执行一段时间后突然无法执行，后台worker日志显示如下提示:
[2018-05-25 17:22:05,068] {jobs.py:2508} INFO - Task is not able to be run查看任务对应的执行日志:
cat /home/py/airflow-home/logs/testBashOperator/print_date/2018-05-25T00:00:00/6.log ... [2018-05-25 17:22:05,067] {models.py:1190} INFO - Dependencies not met for <TaskInstance: testBashOperator.print_date 2018-05-25 00:00:00 [success]>, dependency 'Task Instance State' FAILED: Task is in the 'success' state which is not a valid state for execution. The task must be cleared in order to be run.根据错误提示，说明依赖任务状态失败，针对这种情况有两种解决办法:
使用airflow run运行task的时候指定忽略依赖task:
$ airflow run -A dag_id task_id execution_date使用命令airflow clear dag_id进行任务清理:
$ airflow clear -u testBashOperator
[2018-06-29 09:32:14,622: CRITICAL/MainProcess] Unrecoverable error: PreconditionFailed(406, "PRECONDITION_FAILED - inequivalent arg 'x-expires' for queue 'celery@PQ
SZ-L01395.celery.pidbox' in vhost '/': received the value '10000' of type 'signedint' but current is none", (50, 10), 'Queue.declare')
Traceback (most recent call last):
File "c:\programdata\anaconda3\lib\site-packages\celery\worker\worker.py", line 205, in start
self.blueprint.start(self)
.......
File "c:\programdata\anaconda3\lib\site-packages\amqp\channel.py", line 277, in _on_close
reply_code, reply_text, (class_id, method_id), ChannelError,
amqp.exceptions.PreconditionFailed: Queue.declare: (406) PRECONDITION_FAILED - inequivalent arg 'x-expires' for queue 'celery@PQSZ-L01395.celery.pidbox' in vhost '/'
: received the value '10000' of type 'signedint' but current is none出现该错误的原因一般是因为rabbitmq的客户端和服务端参数不一致导致的，将其参数保持一致即可。
比如这里提示是x-expires 对应的celery中的配置是control_queue_expires。因此只需要在配置文件中加上control_queue_expires = None即可。在celery 3.x中是没有这两项配置的，在4.x中必须保证这两项配置的一致性，不然就会抛出如上的异常。
我这里遇到的了两个rabbitmq的配置与celery配置的映射关系如下表：
| rabbitmq | celery4.x |
|---|---|
| x-expires | control_queue_expires |
| x-message-ttl | control_queue_ttl |
celery升级到4.x之后运行抛出如下异常:
/anaconda/anaconda3/lib/python3.6/site-packages/celery/backends/amqp.py:67: CPendingDeprecationWarning:
The AMQP result backend is scheduled for deprecation in version 4.0 and removal in version v5.0. Please use RPC backend or a persistent backend.
alternative='Please use RPC backend or a persistent backend.')原因解析:
在celery 4.0中 rabbitmq 配置result_backbend方式变了:
以前是跟broker一样:result_backend = 'amqp://guest:guest@localhost:5672//'
现在对应的是rpc配置:result_backend = 'rpc://'参考链接:http://docs.celeryproject.org/en/latest/userguide/configuration.html#std:setting-event_queue_prefix
windows上运行celery 4.x抛出以下错误:
[2018-07-02 10:54:17,516: ERROR/MainProcess] Task handler raised error: ValueError('not enough values to unpack (expected 3, got 0)',)
Traceback (most recent call last):
......
tasks, accept, hostname = _loc
ValueError: not enough values to unpack (expected 3, got 0)celery 4.x暂时不支持windows平台，如果为了调试目的的话，可以通过替换celery的线程池实现以达到在windows平台上运行的目的:
pip install eventlet celery -A <module> worker -l info -P eventlet参考链接:
https://stackoverflow.com/questions/45744992/celery-raises-valueerror-not-enough-values-to-unpack
https://blog.csdn.net/qq_30242609/article/details/79047660
airflow运行中抛出以下异常:
Traceback (most recent call last): File "/anaconda/anaconda3/lib/python3.6/site-packages/airflow/executors/celery_executor.py", line 83, in sync ...... return self._maybe_set_cache(self.backend.get_task_meta(self.id)) File "/anaconda/anaconda3/lib/python3.6/site-packages/celery/backends/base.py", line 307, in get_task_meta meta = self._get_task_meta_for(task_id) AttributeError: 'DisabledBackend' object has no attribute '_get_task_meta_for' [2018-07-04 10:52:14,746] {celery_executor.py:101} ERROR - Error syncing the celery executor, ignoring it: [2018-07-04 10:52:14,746] {celery_executor.py:102} ERROR - 'DisabledBackend' object has no attribute '_get_task_meta_for'这种错误有两种可能原因:
- CELERY_RESULT_BACKEND属性没有配置或者配置错误;
- celery版本太低，比如airflow 1.9.0要使用celery4.x，所以检查celery版本，保持版本兼容;
查看worker日志airflow-worker.err
airflow.exceptions.AirflowException: dag_id could not be found: bmhttp. Either the dag did not exist or it failed to parse. [2018-07-31 17:37:34,191: ERROR/ForkPoolWorker-6] Task airflow.executors.celery_executor.execute_command[181c78d0-242c-4265-aabe-11d04887f44a] raised unexpected: AirflowException('Celery command failed',) Traceback (most recent call last): File "/anaconda/anaconda3/lib/python3.6/site-packages/airflow/executors/celery_executor.py", line 52, in execute_command subprocess.check_call(command, shell=True) File "/anaconda/anaconda3/lib/python3.6/subprocess.py", line 291, in check_call raise CalledProcessError(retcode, cmd) subprocess.CalledProcessError: Command 'airflow run bmhttp get_op1 2018-07-26T06:28:00 --local -sd /home/ignite/airflow/dags/BenchMark01.py' returned non-zero exit status 1.通过异常日志中的Command信息得知, 调度节点在生成任务消息的时候同时也指定了要执行的脚本的路径(通过ds参数指定)，也就是说调度节点(scheduler)和工作节点(worker)相应的dag脚本文件必须置于相同的路径下面，不然就会出现以上错误。
参考链接:https://stackoverflow.com/questions/43235130/airflow-dag-id-could-not-be-found
出现这个错误的原因是因为URL中未提供origin参数，这个参数用于重定向，例如调用airflow的/run接口，可用示例如下所示:
http://localhost:8080/admin/airflow/run?dag_id=example_hello_world_dag&task_id=sleep_task&execution_date=20180807&ignore_all_deps=true&origin=/admin
请务必使用RabbitMQ+CeleryExecutor, 毕竟这个也是Celery官方推荐的做法, 这样就可以使用一些很棒的功能, 比如webui上点击错误的Task然后ReRun
pip install distribution
在使用supervisor的启动worker,server,scheduler的时候, 请务必给配置的supervisor任务加上
environment=AIRFLOW_HOME=xxxxxxxxxx主要原因在于如果你的supervisor是通过调用一个自定义的脚本来运行的, 在启动worker的时候会另外启动一个serve_log服务, 如果没有设置正确的环境变量, serve_log 会在默认的AIRFLOW_HOME里找日志, 导致无法在webui里查看日志
如果在多个机器上部署了worker, 那么你需要iptables开启那些机器的8793端口, 这样webui才能查看跨机器worker的任务日志
celery提供了两种库来实现amqp, 一种是默认的kombu, 另外一个是librabbitmq, 后者是对其c模块的绑定, 在1.8.1版本中, 使用的kombu的时候会出现scheduler自动断掉的问题, 这个应该是其对应版本4.0.2的问题, 当切成librabbitmq的时候, server 与 scheduler运行正常, 但是worker的从来不consume任务, 最后查出原因: Celery4.0.2的协议发生了变化但是librabbitmq还没有对应修改, 解决方法是, 修改源码里的 executors/celery_executor.py文件然后加入参数
CELERY_TASK_PROTOCOL = 1
运行一段时间过后, 由于网络问题导致所有任务都在queued状态, 除非把worker重启才能生效, 查资料有人说是clelery的broker pool有问题, 继续给celery_executor.py加入参数
BROKER_POOL_LIMIT=0 //不使用连接池另外这样只会减少卡死的几率, 最好使用crontab定时重启worker
可以给DAG中的task指定一个queue, 然后在特定的机器上运行 airflow worker -q=QUEUE_NAME 即可实现
celery为了让scheduler知道每个task的结果并且知道结果的时间为 O(1) , 那么唯一的解决方式就是给每一个任务创建一个UUID的queue, 默认这个queue的过期时间是1天, 可以通过更改celery_executor.py的参数来调节这个过期时间
CELERY_TASK_RESULT_EXPIRES = time in seconds
原因：不能用根用户启动的根本原因，在于airflow的worker直接用的celery，而celery 源码中有参数默认不能使用ROOT启动，否则将报错 .
C_FORCE_ROOT = os.environ.get('C_FORCE_ROOT', False)
ROOT_DISALLOWED = """\
Running a worker with superuser privileges when the
worker accepts messages serialized with pickle is a very bad idea!
If you really want to continue then you have to set the C_FORCE_ROOT
environment variable (but please think about this before you do).
User information: uid={uid} euid={euid} gid={gid} egid={egid}
"""
ROOT_DISCOURAGED = """\
You're running the worker with superuser privileges: this is
absolutely not recommended!
Please specify a different user using the --uid option.
User information: uid={uid} euid={euid} gid={gid} egid={egid}
"""解决方案一：修改airlfow源码，在celery_executor.py中强制设置C_FORCE_ROOT
from celery import Celery, platforms
在app = Celery(…)后新增
platforms.C_FORCE_ROOT = True
重启即可解决方案二：在容器初始化环境变量的时候，设置C_FORCE_ROOT参数，以零侵入的方式解决问题
强制celery worker运行采用root模式 export C_FORCE_ROOT=True
在dags中以docker方式调度任务时，为了container的轻量话，不做重型的docker pull等操作，我们利用了docker cs架构的设计理念，只需要将宿主机的/var/run/docker.sock文件挂载到容器目录下即可 docker in docker 资料 :https://link.zhihu.com/?target=http://wangbaiyuan.cn/docker-in-docker.html#prettyPhoto
当时考虑到文件更新的一致性，采用所有worker统一执行master下发的序列化dag的方案，而不依赖worker节点上实际的dag文件，开启这一特性操作如下
worker节点上： airflow worker -cn=ip@ip -p //-p为开关参数，意思是以master序列化的dag作为执行文件，而不是本地dag目录中的文件 master节点上： airflow scheduler -p错误原因: 远程的worker节点上不存在实际的dag文件，反序列化的时候对于当时在dag中定义的函数或对象找不到module_name
解决方案一：在所有的worker节点上同时发布dags目录，缺点是dags一致性成问题
解决方案二：修改源码中序列化与反序列化的逻辑，主体思路还是替换掉不存在的module为main。修改如下://models.py 文件，对 class DagPickle(Base) 定义修改 import dill class DagPickle(Base): id = Column(Integer, primary_key=True) # 修改前: pickle = Column(PickleType(pickler=dill)) pickle = Column(LargeBinary) created_dttm = Column(UtcDateTime, default=timezone.utcnow) pickle_hash = Column(Text)
tablename= "dag_pickle"
def init(self, dag):
self.dag_id = dag.dag_id
if hasattr(dag, 'template_env'):
dag.template_env = None
self.pickle_hash = hash(dag)
raw = dill.dumps(dag)
reg_str = 'unusualprefix\w*{0}'.format(dag.dag_id)
result = re.sub(str.encode(reg_str), b'main', raw)
self.pickle =result
//cli.py 文件反序列化逻辑 run(args, dag=None) 函数
// 直接通过dill来反序列化二进制文件，而不是通过PickleType 的result_processor做中转
修改前: dag = dag_pickle.pickle
修改后：dag = dill.loads(dag_pickle.pickle)
> 解决方案三：源码零侵入，使用python的types.FunctionType重新创建一个不带module的function，这样序列化与反序列化的时候不会有问题new_func = types.FunctionType((lambda df: df.iloc[:, 0].size == xx).code, {})
#### 25、在master节点上，通过webserver无法查看远程执行的任务日志
> 原因：由于airflow在master查看task执行日志是通过各个节点的http服务获取的，但是存入task_instance表中的host_name不是ip，可见获取hostname的方式有问题.
> 解决方案：修改airflow/utils/net.py 中get_hostname函数，添加优先获取环境变量中设置的hostname的逻辑
```python
//models.py TaskInstance
self.hostname = get_hostname()
//net.py 在get_hostname里面加入一个获取环境变量的逻辑
import os
def get_hostname():
"""
Fetch the hostname using the callable from the config or using
`socket.getfqdn` as a fallback.
"""
# 尝试获取环境变量
if 'AIRFLOW_HOST_NAME' in os.environ:
return os.environ['AIRFLOW_HOST_NAME']
# First we attempt to fetch the callable path from the config.
try:
callable_path = conf.get('core', 'hostname_callable')
except AirflowConfigException:
callable_path = None
# Then we handle the case when the config is missing or empty. This is the
# default behavior.
if not callable_path:
return socket.getfqdn()
# Since we have a callable path, we try to import and run it next.
module_path, attr_name = callable_path.split(':')
module = importlib.import_module(module_path)
callable = getattr(module, attr_name)
return callable()